{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This notebook will take you through the functionality available in the notebooks interface for kdb+. Above each group of cells is an explanation, some of the cells are intended to be run, others you will need to change before running them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running code\n",
    "The cells below demonstrate the simplest case of running some code and getting a result. There are 3 things to note here:\n",
    "\n",
    "- Each line of code which would produce output at a console produces output in the notebook.\n",
    "- stderr/stdout are printed separately to the output in the usual way for notebooks, note that if your print statement such as `-1\"hello world!\"` has an output (here -1) then the output will be displayed, you can suppress this with a semicolon at the end of a statement as usual.\n",
    "- Execution is 'script like', i.e. you can use the normal rules of indentation for functions, if/while blocks, and select/update/delete statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42\n"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world!\n",
      "hello quiet world\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "42\n"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "-1\n"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myvar:42                       / this statement has no output\n",
    "myvar                          / will output the value of myvar\n",
    "0N!myvar                       / will output the value of myvar and print it to stdout\n",
    "-1\"hello world!\"               / will print hello world! and output -1\n",
    "-1\"hello quiet world\";         / will print hello quiet world but not print anything to stdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time         sym    price   \n",
       "----------------------------\n",
       "00:34:28.887 A      13.92752\n",
       "01:53:10.321 B      15.17091\n",
       "05:15:26.418 select 15.1598 \n",
       "07:57:14.764 select 14.06664\n",
       "11:32:19.305 B      11.78084\n",
       "11:37:56.091 A      13.01772\n",
       "14:11:00.647 B      17.85033\n",
       "14:52:59.840 select 15.3471 \n",
       "15:59:34.638 A      17.11172\n",
       "16:46:10.161 B      14.11597\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "sym| time         price   \n",
       "---| ---------------------\n",
       "A  | 15:59:34.638 14.68565\n",
       "B  | 16:46:10.161 14.72951\n"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "/\n",
    "indentation examples \n",
    "The usual rules of identation apply \n",
    "Also note this is a block comment, make sure there is just a single / at the start of the first line in this cell\n",
    "\\\n",
    "func:{\n",
    " / this is a multiline function\n",
    " :1;\n",
    " }\n",
    "\n",
    "/ define a table over multiple lines\n",
    "tab:([]time:asc 10?.z.t;\n",
    "       sym:10?`A`B`select;\n",
    "       price:10+10?10f)\n",
    "select from tab\n",
    "t1:100#tab\n",
    "\n",
    "/ now we can do a multiline select\n",
    "select last time,avg price by sym\n",
    " from tab where sym in `A`B\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code completion and getting help\n",
    "\n",
    "The notebook supports code completion of q keywords and anything in the `.h`, `.Q`, `.z` and `.j` directories of q. \n",
    "Completion also works on user defined variables, provided they exist on the server. If you've defined variables in the *same* cell they won't exist yet in the server process before the cell is first executed, but notebooks will complete these for you locally.\n",
    "\n",
    "Code completion in notebooks is accessed via the TAB key."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sel      / q keywords, place your cursor anywhere in the word and hit tab\n",
    "ma       / if you place your cursor in this word and hit TAB you should see a drop down with max, maxs and mavg.\n",
    "select   / completion can replace existing text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets define some variables in directories we can complete on, run the cell below and then try auto completion in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ".dict1.subdir1.foo:1\n",
    ".dict1.subdir1.goo:2\n",
    ".dict1.subdir2.foo:3\n",
    ".dict1.subdir2.goo:4\n",
    ".dict2.value:4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ".dict1.subdir1.f       / hit TAB to complete\n",
    ".dict1.sub             / TAB here gives a list of subdirectories\n",
    "/ and so on and so on...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Help\n",
    "Help is available on q keywords and built in commands, embedPy and Python foreign objects, and user defined variables. For user defined variables the console representation along with datatype information is displayed.\n",
    "\n",
    "In notebooks help is accessed with SHIFT+TAB, this should pop up a window in the notebook, to see an html version of the help with links to the online documentation for the function, hit SHIFT+TAB 4 times in succession (or use the buttons in the help window).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time         sym    price   \n",
       "----------------------------\n",
       "00:34:28.887 A      13.92752\n",
       "01:53:10.321 B      15.17091\n",
       "05:15:26.418 select 15.1598 \n",
       "07:57:14.764 select 14.06664\n",
       "11:32:19.305 B      11.78084\n",
       "11:37:56.091 A      13.01772\n",
       "14:11:00.647 B      17.85033\n",
       "14:52:59.840 select 15.3471 \n",
       "15:59:34.638 A      17.11172\n",
       "16:46:10.161 B      14.11597\n"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select time,sym,price from tab / place your cursor in the 'select' or 'tab' words here and hit SHIFT TAB\n",
    "                               / if you try it in time,sym,price or from you will get \"Sorry no help available\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Errors\n",
    "Errors are displayed in red and fall into 2 classes\n",
    "\n",
    "- Parse errors, parsing of the cell content is checked by the kernel before sending to the server for evaluation, the usual q parsing rules apply. Note that for foreign languages (lines preceded by `p)` for example) parsing is not checked.\n",
    "\n",
    "- Evaulation (runtime errors), these are reported from the server, one important thing to note is that as with q scripts, lines up to where the error occurred will have been executed, e.g.\n",
    "```\n",
    "a:1\n",
    "b:a+`\n",
    "a:2\n",
    "```\n",
    "\n",
    "will result in a having a value of 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "\u001b[0;31mparse error\u001b[0m",
     "evalue": "\u001b[0;31mfrom\u001b[0m",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31mparse error\u001b[0m",
      "\u001b[0;31mfrom\u001b[0m"
     ]
    }
   ],
   "source": [
    "select from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "\u001b[0;31mparse error\u001b[0m",
     "evalue": "\u001b[0;31m{\u001b[0m",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31mparse error\u001b[0m",
      "\u001b[0;31m{\u001b[0m"
     ]
    }
   ],
   "source": [
    "f:{ / this function was not properly indented (on the last line)\n",
    " -1\"hello world\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a:2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "\u001b[0;31mtype\u001b[0m",
     "evalue": "\u001b[0;31mtype\u001b[0m",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31mevaluation error:\n\u001b[0m",
      "\u001b[0;31mtype\u001b[0m",
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31m  [0]  b:a+`  \n          ^\n\u001b[0m"
     ]
    }
   ],
   "source": [
    "a:1    / run once to trigger an error, then execute next cell to see the value of a\n",
    "b:a+`  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1\n"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### System commands\n",
    "\n",
    "System commands can be used with the `\\` operator at the start of a line in a code cell.\n",
    "\n",
    "**NOTE**: \\d does not currently work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\\ls\n",
    "\\p\n",
    "\\c 50 200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and saving code\n",
    "\n",
    "In addition to loading code with `\\l` , code from a script *on the server* can be loaded directly into a cell using a 'magic' command\n",
    "\n",
    "```/%loadscript filename```\n",
    "\n",
    "This will not attempt to execute the code and any code in the rest of the cell will not execute.``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "/ run the cell, the contents of matplot lib will be inserted in place\n",
    "/%loadscript examples/importmatplotlib.q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```/%savescript filename [overwrite]```\n",
    "\n",
    "Will save contents of the cell as a script, optionally overwriting the script if it already exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "/ save the contents of the current cell as a script\n",
    "/%savescript saveexample.q 0\n",
    "aaa:1 2 3\n",
    "bbb:3 4 5\n",
    "func:{\n",
    " \n",
    " }\n",
    "/ comments should be included in the file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python and inline display\n",
    "\n",
    "Along with k and q code, python code can be run in a q process using embedPy, charts created using matplotlib will be displayed inline in the notebook.  The cells below demonstrate a couple of ways we can run python code. \n",
    "\n",
    "The first example uses the p) prefix to define and display a chart, using an example from the matplotlib website.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad388777f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p)import matplotlib.pyplot as plt\n",
    "p)import numpy as np\n",
    "p)t = np.arange(0.0, 2.0, 0.01)\n",
    "p)s = 1 + np.sin(2*np.pi*t)\n",
    "p)plt.plot(t, s)\n",
    "\n",
    "p)plt.xlabel('time (s)')\n",
    "\n",
    "p)plt.ylabel('voltage (mV)')\n",
    "p)plt.title('About as simple as it gets, folks')\n",
    "p)plt.grid(True)\n",
    "\n",
    "p)plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This cell demonstrates loading a python file which exists on the server and does exactly the same as the cell above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAFCCAYAAACzT9ntAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4W+d58P/vDe69RQ1OUcuSrEHKkiU7tpQmtpxRJ22GndSx26SOM9qmaVrHfZs0nen7dv2SZrtN3LSNlcSJY9dDcmxL8pBsSaSGtUVJnBJFce8B4P79gQOZokgKJAGcA+D5XBcuEmfeAA5wn/OsI6qKYRiGYdjFZXcAhmEYRmwzicgwDMOwlUlEhmEYhq1MIjIMwzBsZRKRYRiGYSuTiAzDMAxbmURkzIiIPCYif2t3HKEgIh8XkRdCtG1b3zcReYeInLJr/zMlPj8SkU4R2RfA8ioii6z/o/ZYjRYmERlTEpFd1pc/KUz7K7N+ROLDsb+JqOr/qOoddu0/lFT1VVVd6n8uInUi8q5Q7EtENotIU5A2dyvwbqBIVdcHaZuGQ5hEZExKRMqAdwAK/KatwRixrhSoU9V+uwMxgs8kImMqnwDeAB4D7p9gfr6I/FpEekVkt4iU+meIyCYR2S8i3dbfTWPmXXUWLiJfE5H/tp6+Yv3tEpE+Edk4fqcisl5E9opIl4hcFJFviUiiNU9E5F9FpFVEekTkLRFZOdGLE5EHROScFf95Efn4mOmvjVlOReSzInLGWvZvRKRCRPZY+/jZmP1vFpEmEflzEWmzXuvHJ3uDReR9InLIei17RGTVFMt+Q0QarX1Wi8g7xr0nB6x5l0TkXybZxpWrFBH5L6AE+F/rvf6zSdb5M+t9viAinxpX7JUkIv8kIg3Wfr8nIikikgY8D8y3tt0nIvMDjXPc/j8J/Duw0drOX1nTf19EakWkQ0SeFpH5AWwrQ0R2isg3rWPlPSJy3Ppcm0XkS9fbhhECqmoe5jHhA6gFPgtUAaNA4Zh5jwG9wG1AEvAN4DVrXi7QCdwHxAP3Ws/zrPl1wLvGbOtrwH9b/5fhuwKLnyKuKuBma9tlwAngC9a8O4FqIBsQ4AZg3gTbSAN6gKXW83nACuv/B/yvxXquwFNAJrACGAZeAhYCWcBx4H5r2c2AG/gX6325Hegfs5/HgL+1/l8LtAIbgDh8yb4OSJrkdf8OkGe97j8BWoBka95e4D7r/3Tg5km2sRloGvP8qs9iguW3WvtZAaQC/229H4us+f8KPG195hnA/wJfn2hf04lzgjjGfybvBNqASut9/jfglXGfmT/Gx4C/td67ff7335p3EXiH9X8OUGn39y4WH+aKyJiQiNyKrzjkZ6paDZwFPjZusWdV9RVVHQb+D74z1mLgvcAZVf0vVXWr6uPASeD9wYhNVatV9Q1r23XA9/H94IMvYWYAywBR1ROqenGSTXmBlSKSoqoXVfXYFLv9f6raYy1zFHhBVc+paje+M/+145b/iqoOq+pu4FngIxNs80Hg+6r6pqp6VPU/8SW5myd53f+tqu3W6/5nfD/A/vqeUWCRiOSrap+qvjHFa5mOjwA/UtVjqjqA76QB8F19Wq/hj1W1Q1V7gb8H7plie8GK8+PAD1W1xjr+HsF3/JVNsvx8YDfwc1X9i3HxLBeRTFXtVNWaGcZjzIJJRMZk7sf3Y9tmPf8J1xbPNfr/UdU+oAPfF34+UD9u2XpgQTACE5ElIvKMiLSISA++H798K46XgW8B3wZaReQHIpI5fhvqq2v4KPAQcFFEnhWRZVPs9tKY/wcneJ4+5nmnXl2XUY/vPRmvFPgTq1iuS0S6gOJJlkVEviQiJ8RX3NmF72os35r9SWAJcFJ8RaHvm+K1TMd8xnzO4/4vwHeVVD0m/u3W9MkEK86rjjHr+Gtn8mPsvUAK8L1x038beA9QL77i5WuKgo3QM4nIuIaIpOA7E77d+rFvAf4YWC0iq8csWjxmnXR8xTMXrEcpVysBmq3/+/H9gPnNHfN/IMPBfxffFdZiVc0E/hxfMZxvA6rfVNUqYDm+H70/nWgjqrpDVd+Nr1juJPBoAPsORI5VR+JXgu89Ga8R+DtVzR7zSLWuIK9i1Qf9Gb7PJUdVs4FurNetqmdU9V5gDvB/gSfGxTCZ673fF4GiMc+Lx/zfhi8JrxgTf5aq+pPyNdueRZzjXXWMWdvI4+1jbLxH8SXJ58buT1X3q+rdVjy/An42g1iMWTKJyJjIBwAPvh/yNdbjBuBVfA0Y/N4jIrdaFfV/A7yhqo3Ac8ASEfmYiMSLyEetbT1jrXcIuEdEEkRkHfChMdu8jK/IbOEU8WXgq9/ps65iPuOfISI3icgGEUnAl/CGrO1dRUQKReRu60dpGOibaLlZ+CsRSbQSyPuAn0+wzKPAQ1a8IiJpIvJeEcmYYNkMfHVPl4F4Efkqvjor/+v5HREpUFUv0GVNDuT1XGLq9/pnwO+KyA0ikgp8xT/D2tejwL+KyBwrjgUicueYbeeJSFYgcYqvYccDAcQM8LgV1xrxdS34e+BNq6h2Mp8HTuFrnJFifT4fF5EsVR3Fd0wF8xgwAmQSkTGR+/HVCzSoaov/ga/I6+Pydh+fnwB/ia9IrgpfZTqq2o7vx/dP8BWX/BnwvjHFfF8BKvA1YPgraztY6w4Afwe8bhX3TFRf8iV89VW9+H4IfzpmXqY1rRNf0U078I8TbMMFfBHfmXUHvjqmz0yw3Ey0WPu/APwP8JCqnhy/kKoeAH4f3/vaia9xyAOTbHMHvjP60/he1xBXF5NtBY6JSB++hiP3qOpgALF+HfgL672+psWYqj4PfBPYacXnr9MZtv4+7J9uFZO+iFVvZb3mx4Fz1vbnTxandTKTN2b7U1LVF/EdR7/Ad9VWwdR1U6iq4qvTasLX+CQZX4OaOiv2h/DVPRlhJr7PxjCMYBCRzfhaABZdb9lIJCI34GuskaSq7iBu91bgc1axnRFjzBWRYRhTEpEPiq+/UA6+ep3/DWYSAlDV10wSil0mERmGcT2fxtff6Sy+usNgFWEaBmCK5gzDMAybmSsiwzAMw1YmERmGYRi2sm2o/VDIz8/XsrKyGa/f399PWtpM+taFj9NjdHp8YGIMFhPj7Dk9PphdjNXV1W2qOtVIGz7hHtwulI+qqiqdjZ07d85q/XBweoxOj0/VxBgsJsbZc3p8qrOLETigZtBTwzAMw+lMIjIMwzBsZRKRYRiGYSuTiAzDMAxbmURkGIZh2MokIsMwDMNWIUtEIlIsIjtF5LiIHBORP5pgGRGRb4pIrYgcEZHKMfO2isgpa96XQxWnYRiGYa9QXhG5gT9R1eXAzcDnRGT5uGXuAhZbjwfx3XkTEYnDd6vnu/DdUO3eCdY1DMMwokDIEpGqXlTVGuv/XuAE195P/m7gx1bfpzeAbBGZB6wHalX1nKqOANusZWOamgFqDYcwx6IRTGEZ4kdEyoC1wJvjZi3g6rtMNlnTJpq+IXQROld1fSc/3lvHgbpOLnYPkpkorD2/j49tKOWdy+YQ5xK7QzRixJlLvTy2p449Z9tp7BggOU5ZeXovH1lXzHtXzSMpPs7uEI0IFfLbQIhIOrAb+DtV/eW4ec8A/6Cqr1nPX8J36+EyYKuqfsqafh+wQVU/P8H2H8RXrEdhYWHVtm3bZhxrX18f6enpM14/mPpHlceODbO/xUNKPKzKjyM/xUVb/whnelx0DCkVWS4+vTqJOanOaXPipPdwMibG6RnxKE+cHuGFejcJLrgxP465aS66B0c42+uipV8pTBU+vTqJhVnOSkZOeh8n4vT4YHYxbtmypVpV1113wUDGAZrpA0gAdgBfnGT+94F7xzw/BcwDNgI7xkx/BHjkevuLlrHmzl3u001ff0krHnlW/+2l09o3NHpl3s6dO3XU7dGfH2jUlX+5XZd/5Xl9/cxlG6O9mlPew6mYGAPX3jes7/3mK1r68DP61V+9pe19w1fm7dy5U71er7584pJu/PsXteKRZ/WJA402Rnstp7yPk3F6fKoRPtaciAjwH8AJVf2XSRZ7GviE1XruZqBbVS8C+4HFIlIuIonAPdayUe98Wz/3/GAvg6MefvGZTXz+nYtJS7q6BDU+zsWHqorY/oXbKMpJ5ff+cz+v17bZFLERrTr6R/jYo29w5lIfj35iHX9190py0xKvWkZE2LJsDs//0W3cvDCPLz1xmJ8faJxki4YxsVCW6dwC3Ae8U0QOWY/3iMhDIvKQtcxzwDmgFngU+CyAqrqBz+O7mjoB/ExVj4UwVkfoHhzlgR/tY9SjPP77N7O6OHvK5Rdkp/CT399AaW4aD/74ALWtvWGK1Ih2ox4vD/1XNefb+vn3+9fx7uWFUy6flZrAv9+/jlsX5fPwL46YEyNjWkLZau41VRVVXaWqa6zHc6r6PVX9nrWMqurnVLVCVW9U1QNj1n9OVZdY8/4uVHE6hderfPGnh7jQNcijn6hi6dyMgNbLS0/isd+7iZTEOD79X9X0DbtDHKkRC/7+uRPsq+vg/31oFe9YfP3byQAkJ8Txvd+poqIgnT94/CDNXYMhjtKIFs6p5Y5xP9pTx0snW/nK+5ZTVZo7rXXnZaXwb/dWUtc+wNeejvoLRyPEXj55iR+9Xsfv3lLG3WvG97iYWlpSPN+7r4oRt5c/3nYIr9c08zauzyQiB2jsGOCfdpxiy9IC7ru5dEbb2FiRx6dvW8gT1U28euZykCM0YkXv0Cj/58mjLClM55G7bpjRNioK0vnq+5ezr66Dn+xrCHKERjQyichmqsqfP/kWLoG/++CN+Np4zMwf/sZiFuan8cgv32JwxBPEKI1Y8Y87TtHSM8T//e1VJMbP/Ofhw1VF3Loon394/iQt3UNBjNCIRiYR2ezlk628eqaNL925lPnZKbPaVnJCHH//WzfS1DnIf7x2LkgRGrHi9KVe/vuNej5xcylrS3JmtS0R4eu/dSMjHi//9MKpIEVoRCuTiGzk9nj5+vMnWZifxu/MsEhuvJsX5nHH8kK+t/scbX3DQdmmERu+/twJ0pLi+cK7lgRle8W5qfzupjJ+UdPE8Qs9QdmmEZ1MIrLRz6ubqG3t48+2LiMhLngfxcN3LWNw1MM3XjwTtG0a0W3P2TZ2nrrM57YsImdcX6HZ+OzmRWQmJ/D1508EbZtG9DGJyCajHi/fermWtSXZ3Lli6j4a01VRkM5Hbyrmp/sbTfm8EZBvvHiGwswkHthUFtTtZqUm8LktFbx6po3q+s6gbtuIHiYR2eRXB5tp7hrkD9+5eFYNFCbzmdsr8Kjyg1dMXZExtf11Hbx5voNP31ZBckLwx4r7+IZSclIT+PbO2qBv24gOJhHZwONVvrPrLCvmZ7J5aWCdBaerODeVu9fM5yf76k1dkTGlb71cS15aIveuLwnJ9tOS4vm9W8p5+WQrxy50h2QfRmQzicgGvz7ewvm2fj63ZVFIrob8Prt5EcNuLz/eWx+yfRiR7cTFHnafvszv3VpOSmLoRs7+xKYyMpLizRW6MSGTiGzwo9frWJCdwp0r5oZ0P4vmpLNl6Rx+8mY9w27Tr8i41n/uqSM5wcXHN4TmasgvKyWBD68r5tkjF7nUY+otjauZRBRmxy/08Ob5Du7fVBqWm9o9sKmMtr4Rnj1yMeT7MiJLZ/8ITx5s5oNri8hODV5Lucl8YmMpHlX+500z2oJxNZOIwuw/99SRkhDHR9eF9gzU7x2L86koSOOxPXVh2Z8RObbtb2TY7Q16S7nJlOWn8U5zhW5MwCSiMOoZGuWpw818YO18slITwrJPEeH+TWUcaermaLOpKDZ8vF7lJ/vq2bgwL+CR3oPhfusK/YVjl8K2T8P5TCIKo6cPXWBo1Ms9N4Xnasjv7jULSIp3sW2/KRIxfPaea6exY5B71heHdb+3LspnQXYKP91vbp5nvM0kojD62YFGls3NYFVRVlj3m5WSwHtvnMdTBy+YwVANwFcsl5WSEPIGM+O5XMJHbyrmtdo2GjsGwrpvw7lMIgqT4xd6ONLUzT03FYe0yfZkPnJTMb3Dbp57yzRaiHWd/SPsONrCB9cuCEkH1uv5UFURIr4TM8MAk4jC5onqJhLjXHxg7fRuNBYsG8pzKctL5efV5ssf6/73yAVGPF4+si68xXJ+87NTuG1xAb+objI3zjMAk4jCwu3x8vThC7xz2ZywNJOdiIjwwbVFvHm+g4vd5hbOsezJg80sm5vB8vmZtsXwW5ULuNA9xL66DttiMJzDJKIweP1sO219w7ZdDfndvWY+qr5GE0Zsqm/v52BDl+3H4ruXF5KaGMdTh5ptjcNwBpOIwuCpg81kJsezZVloxpULVFl+GmuKs3nyoPnyx6qnDl1ABH5z9Xxb40hNjOfOFXN59shF06fICF0iEpEfikiriBydZP6fisgh63FURDwikmvNqxORt6x5B0IVYzgMjnjYfqyF966aR1J8+CuGx/vg2gWcbOnlVEuv3aEYYaaq/OpQMxvKc2d9N+Bg+MDaBfQMudl58rLdoRg2C+UV0WPA1slmquo/quoaVV0DPALsVtWxBcZbrPnrQhhjyO081crAiIf323wG6veeG+chgmk9F4NOX+rj3OV+xxyLt1TkkZuWyPNHzbEY60KWiFT1FSDQmsh7gcdDFYudnn3rIvnpiWwoz7M7FAAKMpK4qSyXHcda7A7FCLPtR1sQgTuWh7fv0GTi41y8+4ZCXj7RaornYpyohq75pIiUAc+o6soplkkFmoBF/isiETkPdAMe4Puq+oMp1n8QeBCgsLCwatu2bTOOt6+vj/T09BmvP96wR/mDlwe4ZX48969ICso2gxHjC3Wj/OTkCP/wjhTmpgX3XCTY72EoxGqMX3l9kJR4+PMNwSmWC0aMhy+7+dfqYb5YlcSqgvigxDWW0z9rp8cHs4txy5Yt1QGVaqlqyB5AGXD0Ost8FPjfcdMWWH/nAIeB2wLZX1VVlc7Gzp07Z7X+eM+/dUFLH35GXztzOWjbDEaMTZ0DWvrwM/rdXbWzD2icYL+HoRCLMda19Wnpw8/oo6+cDdo2gxHj0KhbV3x1uz78xOHZBzQBp3/WTo9PdXYxAgc0gN9uJ7Sau4dxxXKq2mz9bQWeBNbbENesPfdWC7lpiWwoz7U7lKssyE5hVVEW24+a4rlY4S+KDfeQPteTFB/HlmVzeOH4JTymc2vMsjURiUgWcDvw1JhpaSKS4f8fuAOYsOWdk416vOw81cq7bygkPs4J+f5qd66Yy6HGLtO5NUZsP9rCygWZFOem2h3KNbaumEtH/wj7TefWmBXK5tuPA3uBpSLSJCKfFJGHROShMYt9EHhBVfvHTCsEXhORw8A+4FlV3R6qOENl//kOeofc/MYNc+wOZUJbV/rOjM1w/NHvUs8QNQ1dbHXY1ZDf5qUFJMa7zBV6DAt+7aBFVe8NYJnH8DXzHjvtHLA6NFGFz4snWkmMd3Hr4ny7Q5lQRUE6i+eks/1oC/eH6cZohj1esIrl/CcfTpOWFM9tiwt44VgLf/n+5bYMCmzYy3llRlFAVXnp5CU2VeSRmhiyXD9rW1fOZV9dBx39I3aHYoTQ9mMtVBSksWhO+G6AN11bV87lQvcQb5mbN8Ykk4hC4OzlPurbB/iNGwrtDmVKd66Yi8ervHjcFM9Fq87+Ed441+HYqyG/d90whziX8LwpnotJJhGFwIsnWgH4jWXOrB/yWzE/kwXZKbx00iSiaLX79GU8XuXdDunEOpnsVF/r0pet744RW0wiCoGXTlxi+bxMR4znNRUR4falBbxe286I22t3OEYI7DrVSl5aIqsWhPeuwDOxeWkBpy71cqHLtOSMNSYRBVln/wjV9Z28y6Gt5cbbvKSAvmE31fWddodiBJnXq7xypo3blhTgcjm/AcDmpb7vzO7TZhDUWGMSUZDtPNWKV+GdDq8f8tu0KJ+EOGHXaVMkEm3eau6mo3+E25fYe/uRQC2ek868rGR2nTLHYqwxiSjIXjrRSkFGUkQUhQCkJ8WzrjSX3afMWWi02XXqMiJwW4QkIhFhsykqjkkmEQXRqMfLK6cv886lcyKiKMRv89ICTrb00tI9ZHcoRhDtOt3KqqJsctPsuT39TNy+ZA59w25qGkxRcSwxiSiIDjV20TvsZvPSyDgD9bvdine3KZ6LGp39Ixxq7GJzhFwN+d2yKI94l7DLXKHHFJOIgujVM224BDZVOHM0hcksLcxgbmay+fJHkVdr21Al4k6KMpITWFeWY+qJYoxJREH06pnLrCrKJis1we5QpsVfNv/amTZGPaZsPhrsOtVKTmoCq4qy7Q5l2m5fMscUFccYk4iCpHtwlMONXbzDoWPLXc/tSwroHXZTY5pxRzyvV3nl9GXesbiAuAiqq/TbbIqKY45JREGy92w7XoV3LI6sohC/Wxbn+8rmTR+OiHfsQg9tfSMRVyznt2yuKSqONSYRBcmrZy6TlhjH2pLIKwoByExOoLI0xzTjjgKvnPF9hpHSbHs8EeH2Jb6iYrcpKo4JJhEFyWu1bdy8MI8EB94EL1C3Lsrn+MUeOs1o3BHt9do2bpiXSX56kt2hzNgti/PpHXZz9EKP3aEYYRC5v5oO0tgxQH37QMTWD/ndsigPgDfOtdsciTFTQ6MeDtR3cktFnt2hzMrGhb7495xtszkSIxxMIgqCV8/4viy3Rmj9kN+qomzSEuN43Xz5I1ZNfScjbi+bFkV2IirISGJpYQZ7as1JUSwwiSgIXj1zmflZyVQUpNkdyqwkxLlYX57LnrPmyx+pXj/bRpxLWF8e2YkIYNOiPPbXdTDs9tgdihFiJhHNkser7Dnbzq2L86PiFsebKvI5d7nf9OGIUHvOtrO6KIv0JOfeGThQmyryGXZ7qanvsjsUI8RMIpqlo83ddA+ORnyxnJ+/SMeUzUee3qFRjjR1R9zIHpNZX56LS2CvORajXsgSkYj8UERaReToJPM3i0i3iByyHl8dM2+riJwSkVoR+XKoYgyGvVbF/qYIrxz2u2FuJjmpCaZ4LgLtO9+Bx6sRXz/kl5WSwI1F2eZYjAGhvCJ6DNh6nWVeVdU11uOvAUQkDvg2cBewHLhXRJaHMM5ZeeNcO4vnpEd0U9mxXC5hY0Uee2rbUFW7wzGm4fXadpLiXVSW5NgdStBsqsjjUGMX/cNuu0MxQihkiUhVXwE6ZrDqeqBWVc+p6giwDbg7qMEFidvjZf/5Dm5eGB1noH4bK/K50D1EffuA3aEY07DnbBvrynJIToizO5SguaUiH7dX2Vc3k58SI1LYXUe0SUSOiMjzIrLCmrYAaByzTJM1zXGOXuihf8QTdYnI3wfFNOOOHO19w5xs6Y2a+iG/qtIcEuNc7Kk1x2I0s7NpTQ1Qoqp9IvIe4FfA4uluREQeBB4EKCwsZNeuXTMOqK+vb1rrP3fONwKB5+JJdnWcmvF+p2O6Mc6EqpKTJDy19wQLBs9Pa91wxDdb0Rjjvou+oqvk7np27WoKUVRXC9f7uDALXjhczy1p0x8E1emftdPjgzDFqKohewBlwNEAl60D8oGNwI4x0x8BHglkG1VVVTobO3funNby9//wTX3XP++a1T6na7oxztQf//SgVv71C+rxeKe1Xrjim41ojPGRXx7RFV/drqNuT2gCmkC43sdvvHhay778jHb0DU97Xad/1k6PT3V2MQIHNIDfbtuK5kRkrlgdb0RkPb5iwnZgP7BYRMpFJBG4B3jarjgnE631Q34bF+bR3j/C6dZeu0MxAvDG2XbWl+cSH8FjHU5mY0UeqvDmedN6LlqFsvn248BeYKmINInIJ0XkIRF5yFrkQ8BRETkMfBO4x0qibuDzwA7gBPAzVT0WqjhnKlrrh/z8r2v/eVNJ7HStvUOca+tnQ3mu3aGExKqiLJLiXew7b+6VFa1CVkekqvdeZ/63gG9NMu854LlQxBUs/oFBNyyMzi9/UU4K87KS2VfXyX0by+wOx5jCgTrfD/T6KE1ESfG+26vsNy3nolb0XceHSbT1HxpPRFhfnsu+8+2mP5HD7TvfQUpCHCsXZNkdSsisL8/j2IVueodG7Q7FCAGTiGYg2uuH/NaX53KpZ5iGDtOfyMnePN9BVWlORN8L63o2lOfiVag2t7KPStF75IZQtNcP+fnrHN409USO1T0wysmWnqgtlvNbW5JNvEvYZ47FqGQS0QxEe/2QX0VBOrlpiebL72AH6jtQjd76Ib/UxHhuLMoyx2KUMoloBvaf72BhQVrU1g/5iQg3leWYL7+D7TvfQWKcizXF2XaHEnLry3M53NTF0Ki5P1G0MYlomrxepbqhk5tKo/sM1G99eR4NHQNc7B60OxRjAm+e72B1cVZUjS83mQ3luYx6lIMN5v5E0cYkomk6e7mProFR1pVFzwjHU/HXE5mrIufpH3ZztLk76ovl/KpKcxExx2I0MolomvZbfTbWlcXGl/+GeZmkJ8WbL78DHWzowu3VqLgteCCyUhK4YW4m++rMCAvRxiSiaTpQ30F+eiJleal2hxIWcS5hnakncqR959txiW+E6lixvjyX6vpORtxeu0Mxgsgkomk6UNfJutJcrGHyYsL68lzOtPbR3jdsdyjGGG+e72DlgizSk+wcRD+8NpTnMjTq5eiFbrtDMYLIJKJpaO0ZoqFjIGbqh/z89UT+YknDfsNuDwcbu7gpRoqI/W4ydZZRySSiaThQH1v1Q34rF2SRGO+iut58+Z3i2IUeRtxe1sVQsRxAfnoS5flpV8bXM6KDSUTTsL+ug+QEFyvmZ9odSlglxcexakGWGV7FQWqsz6IyxhIR+OrEaho6zRiIUcQkomk4UNfJmuLsqB7TazJVpTkcbe4xnQkd4mBDFwuyUyjMTLY7lLCrKs2ho3+E8239dodiBEns/aLOUP+wm+MXe2KuTN6vqjSHEY+Xo82mktgJaho6Y/JqCLhSHGmu0KOHSUQBOtTYhcerMdVUdiz/j94B8+W33cXuQS52D1FZEv3D+kykoiCdzOR4k4iiiElEAdpf14FIbJbJw9uVxObLb7+aet8QN5UlsXksulxCVWmOORajiElEAaqu72TZ3EwykxPsDsU2lSU51NSJ6/C6AAAgAElEQVSbSmK71TR0khTv4oZ5sdVoZqyq0hzOtPbRNTBidyhGEJhEFAC3x0tNfWfMNZUdb11ZDu39I9S1mxvl2ammoZNVRb4m9bHKXzJhBkCNDrF7JE/DyZZe+kc8MdeRdbwqU0lsu2G3h2PNPTFbLOe3pjibOJeYYzFKmEQUgAN1vo6csdpizm/RlUpi07HVLkebexjxeFkb44koNTGe5fMyOWCOxagQskQkIj8UkVYROTrJ/I+LyBEReUtE9ojI6jHz6qzph0TkQKhiDNT++k7mZyUzPzvF7lBs5XIJlaaS2FYHG/wdWWOzxdxYVaU5HG7sZtRjBkCNdKG8InoM2DrF/PPA7ap6I/A3wA/Gzd+iqmtUdV2I4guIqnKgriPmhvWZzLrSHE5f6qN7YNTuUGJSTUMnRTkpzMmIvY6s41WV5jA46uHExR67QzFmKWSJSFVfASa9blbVParqP7V+AygKVSyz0dQ5yKWe4ZivH/LzVxLXNJqrIjvU1HfFfP2Qn6mzjB5TJiIRKRKRL4nIUyKyX0ReEZHviMh7RSSYSeyTwPNjnivwoohUi8iDQdzPtPnLoNfFyK3Br+dKJbEZdDLsLnQN0tITux1Zx5ufncL8rGTTyToKyGR9QkTkR8AC4BngANAKJANLgC1AFfBl68pnsm2UAc+o6sopltkCfAe4VVXbrWkLVLVZROYAvwb+YLL9WInqQYDCwsKqbdu2TfV6p9TX10d6evpV0358fJg9zW6+865UXA64B9FEMYbb1/YMkhIPD6+/ts7MCfFdT6TGuO+im+8cHuYvNyZTnhVnU2Rvc8L7+J1DQ9R2efmXzRPfqNIJMU7F6fHB7GLcsmVLdUDVK6o64QNYOdk8a34isOg6y5QBR6eYvwo4CyyZYpmvAV+aaj/+R1VVlc7Gzp07r5n2nm+8oh97dO+sthtME8UYbn/51FFd9hfP64jbc808J8R3PZEa49eePqpL/+K5Cd93OzjhffzRa+e09OFntKlzYML5TohxKk6PT3V2MQIHNIDf7qmK1+4SkUnrbVR1RFVrr58TJyYiJcAvgftU9fSY6WkikuH/H7gDmLDlXagNjLg52dLL2mJTJj+Wv5L45MVeu0OJKTUNXaxaEJujv0+myioyN/VEkW2qI3o+sFdEXhWRz4pIwXQ2LCKPA3uBpSLSJCKfFJGHROQha5GvAnnAd8Y10y4EXhORw8A+4FlV3T6tVxUkR5q68XjVNJUd5+1KYtOHI1yGRj0cv9DNWnMsXuWGeRmkJMRduT+TEZkmvdm9qv6xiHwRuA24B/iKlRweB36pqlOeDqvqvdeZ/yngUxNMPwesvnaN8Kux+myYK6Krja0kfuCWcrvDiQlHm7sZ9ahpMTdOfJyLNcXZpmNrhJvyGt8q5tutqp/B17z6X4EvAJfCEZzdauq7KM9PIyct0e5QHKeyNMechYaR/6TIJKJrVZXmcOJiL/3DbrtDMWYooMJmEbkR+Gvg28Aw8Egog3ICVeVQYydrTVPZCa0rzeFC9xAXugbtDiUm1NR3UZybQkFGkt2hOE5VWQ4er3K40QyAGqkmTUQislhEviIix4D/AfqBO1T1ZlX9RtgitEljxyBtfSPmDHQSppI4fFTVd0dWcyxOqLLYdGyNdFNdEW0HkoCPquoqVf17q/4mJlypHzJXRBNaNi+D5ATXlffJCJ3mrkFae4dNIppEVmoCi+akm2Mxgk3VWKFi7HMRyRy7vKpGde3gwYZOUhPjWFqYYXcojpQQ52JVUTY15n4wIed/j00imlxlSTYvHL+EqiIO6HhuTM9164hE5NMi0gIcAaqth+0jYodaTUMXq4uyiTd9NiZVWZLD8QvdDI167A4lqtXUd5Kc4GLZPHNSNJnKkhy6BkY519ZvdyjGDATyK/slfKMslKlqufVYGOrA7DQ44hvR1xTLTa2yJJtRj3K0udvuUKLawYZOVhWZjqxTuTIYr6knikiBHNlngZi6N/Rbzd24vabPxvVc+fKbsvmQGRr1cOxCz5VOxMbEFhWkk5Ecb4qKI9SkdURjPALsEZE38TXdBkBV/zBkUdnM/8O6xlwRTSk/PYmS3FRq6s2XP1TMSVFgXC5hTXH2lRsHGpElkCui7wMv47tnUPWYR9Q62NBJaV4q+emmz8b1VJZkU9PQ6R+g1ggyf1GTKSa+vsqSHE5d6qV3yNy0MdIEckWUoKpfDHkkDuHrs9HFrYvy7Q4lIlSW5vCrQxdo7hqkKGfiofiNmasxJ0UBqyzNQRUON3Zz62Lz/Y0kgVwRPS8iD4rIPBHJ9T9CHplNmjoHudw7bM5AA+QvMjJl88HnPykyxXKBWVPs+86aOsvIE8gVkX/w0rHD+igQlS3nDjaaPhvTsWzu26Mf/+bq+XaHE1X8J0XmjqyByUpJYLHp2BqRrpuIVDWmhle+0mdjrumzEYj4OBerirJMJXEIvD26hzkpClRlSQ7bj7Xg9Soul+nYGimmGmvu1qlWFJFMEZn0FuCR6mBjF6tMR9ZpqSzN4diFHtOxNcgONnSRmhhnToqmoao0h+5B07E10kz1a/vbIrJHRL4qIu8VkfUicpuI/J6I/BfwDJASpjjDYsSjHL/QbYrlpqmyJAe3V3nLdGwNqpqGTlYVZZmTomnw38TSFM9FlkmPcFX9Y+B9wEXgw8DfAF8EFgPfV9XbVHV/WKIMk/oer3XzMVMmPx3+hh2mV3vwDHuU4xd6zEnRNC3MTyczOd4UFUeYKeuIrIFNH7UeUa+2ywuYMvnpyk9PojQvlZqGTpYW2x1NdKjr9pqOrDPgcglrS3JMJ+sIY675xzjb5TE3H5uhqpIcquu7TMfWIKnt8tW3mW4E01dZksPp1l56TMfWiGESkUVVqe3ymjPQGVpbmkNb3zBtgyYRBUNtl5eyvFTyTEfWaasszUYVDpm+bRHDJCLLxe4huoaVtcXmDHQm/PVq/uJNY+ZUlbNdHnNSNENrirMRMQ0WIkkg9yNKtW4Z/qj1fLGIvC+A9X4oIq0icnSS+SIi3xSRWhE5IiKVY+ZtFZFT1rwvT+cFzZT/oK00oxzPyNLCDFIT464UKRkz19gxSM+I7yrTmL6M5ASWzMkwo31EkECuiH6Eb9TtjdbzZuBvA1jvMWDrFPPvwtcCbzHwIPBdABGJA75tzV8O3CsiywPY36zU1HeR4IIb5mWGeldRKT7OxeqibM6aK6JZu3JSZOqHZqyy1DcSt9fUWUaEQBJRhar+P2AUQFUHgOt2WVbVV4Cpbid+N/Bj9XkDyBaRecB6oFZVz6nqCLDNWjakDjZ2Up7lMjcfm4XK0mwae70MjpirotmoaegkKQ5zm/pZWFuSQ++Qm4v9JhFFgkDGmhsRkRR848shIhWMuS/RLCwAGsc8b7KmTTR9w2QbEZEH8V1RUVhYyK5du6YdiNurHGsa4LZ5OqP1w6mvr8+xMSZ0u/Eo/PiZXSzNjbM7nEk5+T0E2H1skNJ05bVXX7E7lCk5+X109/muzI+1DDg2RnD2e+gXjhgDSUR/CWwHikXkf4BbgAdCGdR0qOoPgB8ArFu3Tjdv3jyj7dTc4mbn7leZ6frhsmvXLsfGuKp/hP+v5td4c8vYvLnC7nAm5eT3cGDETdMLL3BXWaJjY/Rz8vvo9Sr/t/rXNA2pY2MEZ7+HfuGIMZBBT38tIjXAzfiK5P5IVduCsO9mYGz3xyJrWsIk00MqPSmejEQzSOJs5KYlUpgqprXSLBxp6sbjVRZlmyLi2fB1bM3mdHMwfqqMUAuk1VwlUIpvqJ8LQImIVIhIIFdTU3ka+ITVeu5moFtVLwL7gcUiUi4iicA91rJGBFiUHcdBc8fWGfMn8Yps5xZtRorKkhwu9Cndg6Zjq9MFkky+A1QCR/BdEa0EjgFZIvIZVX1hopVE5HFgM5AvIk34ivgSAFT1e8BzwHuAWmAA+F1rnltEPg/sAOKAH6rqsZm+QCO8KrJdvH5hhMaOQUryzB1bp6umvovy/DQyEu2OJPL5+2Edauzi9iUFNkdjTCWQRHQB+KQ/GVhNqf8a+DPgl8CEiUhV751o+pj5CnxuknnP4UtURoTxFynVNHSaRDRNqsrBhk5uX1oAmD4ws7W6OAvBNxivSUTOFkhB9JKxVySqehxYpqrnQheWEamKMlykJcaZeqIZaOgYoL1/xIyoECQZyQkUZbjMsTgDbo+XUU/4+gQGkoiOich3ReR26/Ed4LiIJGH1LTIMP5cIq4uzzZd/Bt7uyGoSUbBUZLs41NiF12vqLKfjQH0nN35tB9X1U3UFDZ5AEtED+OpxvmA9zlnTRoEtoQrMiFyVJTmcuNjLwIjb7lAiSk19F2mJcSw1d2QNmkXZLnqH3NRe7rM7lIhS09DJ0KiXhfnpYdlfIM23B4F/th7jmU/XuEZlaTYer3KkqZubF+bZHU7EqGnoZE1JNnEu040gWBZZrQ9r6jtZYkaqCNjBhi4W5qeRkxaeVjOBNN9eLCJPiMhxETnnf4QjOCMyrS32FS2Z4rnADYy4OdnSa4rlgqwwVchJTTDH4jT4G82sCeNYh4EOevpdwI2vKO7HwH+HMigjsuWkJbIwP83cJXMaDjf6OrKaRBRcItYdW81I3AFr6hykrS+8jWYCSUQpqvoSIKpar6pfA94b2rCMSOf78puOrYHyn7GbO7IGX2VJNrWtfXQNjNgdSkSw41gMJBENi4gLOCMinxeRDwLhqcEyIlZlaTYd/SPUtw/YHUpEqKnvZGFBGtmppidrsPnP7A82mquiQBxs6CI1MS6so78Hkoj+CEgF/hCoAn4H+EQogzIin//Lb8rmr09VOdjYZYrlQmR1cTYugYP15lgMRE1DJ6uKsogP4y1xAtlTmar2qWqTqv6uqv42UBLqwIzItqQwg/SkeJOIAlDXPkCH6cgaMmlJ8Sydm2nqiQIwNOrh+IWesB+LgSSiRwKcZhhXxLmENcXZpsFCAGrq/bepN/VDoVJZks2hxi48pmPrlN5q7sbtVdY6JRGJyF0i8m/AAhH55pjHY/ha0BnGlCpLsjnZ0kP/sDlcplLT0El6UjyL55h+LqFSWZJD37CbM629dofiaAdtajQz1RXRBaAaGLL++h9PA3eGPjQj0q0tzcGrcLjJXBVNpaahizXFpiNrKFWWWnWW5gp9SjX1XZTkppKfnhTW/U6aiFT1sKo+BlSo6n+OefxSVU3Bv3FdlVbH1oOmbH5SfcNuTrX0UGmabYdUWV4quWmJps5yCqpKTUOnLcfipEP8iMhbgFr/XzNfVVeFLiwjGmSlJlBRkHalDsS41pHGLrzqu3o0QkdEWGsG453She4hWnuHr1w9htNUY829L2xRGFGrsiSHl062oqoTntDEuisjbhebRBRqlaW+Y7FrYMT015rAlfohG47FqYrm6v0PfPVEN1qPQWuaYVxXZWkOHf0j1JmOrROqaeiioiCNrNQEu0OJelc6tpqi4gnV1HeRnOBi2bzwN5oJZNDTjwD7gA8DHwHeFJEPhTowIzpc6dhqiueu4fX6yuSrTLFcWKwuziLOJaZ4bhLVDZ2sLsomIYwdWf0C2eP/AW5S1ftV9RPAeuAroQ3LiBaL56STYTq2TuhcWx9dA6OsK821O5SYkJoYz7K5GeZYnMDgiIdjzd22nRQFkohcqto65nl7gOsZBi6XsKYk2/Rqn0D1lY6s5oooXCpLcjjUYDq2jnekqQu3V1lX5txEtF1EdojIAyLyAPAs8FwgGxeRrSJySkRqReTLE8z/UxE5ZD2OiohHRHKteXUi8pY178B0XpThLGtLcjjV0kOf6dh6lQN1nWRbLQuN8KgszaZ/xMPpS6Zj61gH6u1rqAABJCJV/VPg+8Aq6/EDVX34euuJSBzwbeAuYDlwr4gsH7ftf1TVNaq6Bt+wQbtVdexN0rdY89cF/IoMx6ksycarvqbKxtuqGzqpKskxrQnDyAzGO7Ga+k4qCsJ3R9bxAmms8EXgTVX9ovV4MsBtrwdqVfWcqo4A24C7p1j+XuDxALdtRBBzx9ZrdfSPcO5yP1U2FYXEqpLcVPLSEs0IC2N4vUp1Q6etdZWBFM1lAC+IyKvW/YgKA9z2AqBxzPMma9o1RCQV2Ar8YsxkBV4UkWoReTDAfRoOlJWawKI56aaeaAx/K8IqM+J2WPnv2HrQnBRdca6tn66BUVtbb0qgd9AUkVXAR4HfBppU9V3XWf5DwFZV/ZT1/D5gg6p+foJlPwr8jqq+f8y0BaraLCJzgF8Df6Cqr0yw7oPAgwCFhYVV27ZtC+j1TKSvr4/0dGff88/pMU4W33+8NUxNq5tvvTPV9qIoJ7yHPz81wva6Ub77rlQS4659P5wQ4/VEaozPnBvhidOj/Ns7U8lINMfi7qZRfnR0hK/fmsK89GuvTWYT45YtW6oDqlpR1YAewFzgD4DXgSMBLL8R2DHm+SPAI5Ms+yTwsSm29TXgS9fbZ1VVlc7Gzp07Z7V+ODg9xsnie/zNei19+Bmtbe0Nb0ATcMJ7+OHv7tHf/NZrk853QozXE6kx7j3bpqUPP6MvHm8Jf0DjOOE9/NOfH9I1f7VDvV7vhPNnEyNwQAPIL4HUEX1WRHYBLwF5wO9rYOPM7QcWi0i5iCQC9+AbuXv89rOA24GnxkxLE5EM///AHcDRAPZpONTbox+bIpERt5fDTV2sM822bbGqyHRsHetAva9TtZ0lFVONNedXDHxBVQ9NZ8Oq6haRzwM7gDjgh6p6TEQesuZ/z1r0g8ALqto/ZvVC4EnrjYkHfqKq26ezf8NZFhWkk5EcT01DFx9eV2x3OLY6dqGbYbfXjKhgk9TEeG6Yl2EaLPB2o5kPVRXZGsd1E5GqzvhurKr6HOP6HI1JQP7njwGPjZt2Dlg90/0azuOy7thqKonf7shqEpF9KktyeKK6CbfHS7wNQ9o4hVMazcTuJ2CEXVVpDqcu9dI7NGp3KLaqaeikKCeFwsxku0OJWZUlOQyMeDgV4x1bqxs6iXcJq4vtvR+WSURG2FSW5KAKhxu77Q7FNqrKgbpOUz9ks7c7tsZ28Vx1fScrFmSRnBBnaxwmERlhs6YkG5HY7tja1DlIa++wKZazWXFuCvnpSRyM4cYzI24vhxud0WjGJCIjbDKTE1g8Jz2mE9Hb9UNmxG07iQiVJbF9x9bjF3sc02jGJCIjrCpLcjjY0IU3Rkc/rq7vJD0pnqVzw3/zMeNqlaU51LUP0N43bHcotjhQ5xvW0yQiI+ZUluTQPTjKubb+6y8charrO1lbkk2cywx0ardYv2OrkxrNmERkhFVlqa91TiwWifQNuznZ0nPlB9Cw16qiLOJjtGOrv9GME66GwCQiI8wW5qeTmRwfk/2JDjV04VVsu/mYcbXkhDiWz8+MyUTkbzTjhIYKYBKREWYul2/041js1X6gvgOXwBqb+2wYb6ssyeFwYzduj9fuUMLKaXcHNonICLt1pTmcbu2la2DE7lDC6kBdJ0vnZpKRnGB3KIalqjSHwVEPxy/22B1KWO2v6yA9KZ5lczPtDgUwiciwwfryXFR9P8yxYtTjpbq+kw3lptm2k6y3Po995zuus2R02Xe+g3VlOY5pNGMSkRF2q4uzSYxzsa8udr78R5u7GRz1mETkMIWZyZTlpfJmDCWi9r5hzrT2XUnCTmASkRF2yQlxrCnOjqkvv/+M+yYHffkNn/Xlueyv64iZvm37rZIIJ50UmURk2GJ9eS5Hm7vpH3bbHUpY7DvfQUVBGvnpSXaHYoyzvjyProFRzrT22R1KWOw730FSvIsbFzin0YxJRIYt1pfn4vFqTDSd9XiVfXUdrC/PszsUYwIbrtQTtdscSXjsq2unsiSHxHjn/Pw7JxIjplSW+ipKY6GS+FRLL71DbkcVhRhvK8pJYV5WckwUFfcMjXL8Qo+j6ofAJCLDJulJ8aycnxkTX37/mbbTvvyGj4iwvjyXfec7UI3ueqLq+k686qz6ITCJyLDR+vJcDjV2MTTqsTuUkNpX10FRTgrzs1PsDsWYxPryXFp7h6lvH7A7lJDad76DeKtTuZOYRGTYZn15HiNuL0eaovdGearKvvMd5mrI4TbESH+ifec7WFWURUqivTfCG88kIsM2N1ljrkVzJfG5tn7a+kYcVxRiXK2iIJ3ctMSoLioeHPFwpKnLkY1mTCIybJOdmsiyuRnsi+IRFvxn2E788htvExHWl/n6E0Wrg42djHrUkSdFIU1EIrJVRE6JSK2IfHmC+ZtFpFtEDlmPrwa6rhEd1pfnUl3XEbWDTu4730FBRhJleal2h2Jcx/ryXBo6BrjYPWh3KCGx/3wnIlDlwNHfQ5aIRCQO+DZwF7AcuFdElk+w6KuqusZ6/PU01zUi3PryXPpHPBy9EH2DTqoqb55rZ31ZLiLOGNPLmJy/Hu/Nc9F5VfTGuXZumJtJpgMH3Q3lFdF6oFZVz6nqCLANuDsM6xoR5OaFviKrPWfbbI4k+OraB7jQPcTGClMsFwlumJdJVkpCVB6LQ6Meqhs62eTQYzGUiWgB0DjmeZM1bbxNInJERJ4XkRXTXNeIcPnpSSybm8Hes9HXYMH/g3bLonybIzECEecSNi7MY08UHos19Z2MuL2OPRbjbd5/DVCiqn0i8h7gV8Di6WxARB4EHgQoLCxk165dMw6mr69vVuuHg9NjnEl8xUnD7D7by69f3klCGIalD9d7+KtDQ+QmC3Vv7aN+mkVzTv+cITpjzPeO0tQ5ws+ee5k5qaFvyxWu9/CJ0yPECQw1HWNXy/FprRuWGFU1JA9gI7BjzPNHgEeus04dkD+TdVWVqqoqnY2dO3fOav1wcHqMM4nv18datPThZ3Tv2bbgBzSBcLyHHo9X1/71C/rFnx6a0fpO/5xVozPGM5d6tfThZ/TxN+tDE9A44XoPP/Dt1/S3vvP6jNadTYzAAQ0gX4Qy5e8HFotIuYgkAvcAT49dQETmilWLKyLr8RUVtgeyrhE91i/MxSWwpzZ6yuZPXeqlo3/EsWXyxsQqCtKYk5EUVcVzvUOjHGnqdvSxGLKiOVV1i8jngR1AHPBDVT0mIg9Z878HfAj4jIi4gUHgHiuLTrhuqGI17JWZnMCNRdnsOdvOF+0OJkhet5LqpkXO/fIb1xIRNlXk8VptO6oaFa0d953vwONVRzeaCWkdkao+Bzw3btr3xvz/LeBbga5rRK9bKvL4wSvn6B92k5Zkd9Xl7O09287C/DTmZZnx5SLNpkX5/OrQBc609rGkMMPucGbt9dp2kuJdVDpsfLmxzMgKhiNsqsjHbd23J9K5PV7ePN/h6DNQY3L+IqzXo6SoeM/ZNtaV5ZCc4Kzx5cYyichwhHVlOSTGuaKinuhIczd9w27HNpU1plaUk0ppXmpU1BO19Q1zsqWXTRXOPhZNIjIcITkhjsrS7Kj48vuTqb+zrhF5NlXk8ca59ogfeuqNc77vk5MbKoBJRIaDbKrI5/jFHjr7R+wOZVb2nG3nhnmZ5KYl2h2KMUMbK/LpHXJzLMKHnnq9tp2MpHhuXJBldyhTMonIcIxbFuWhCnvPRe5V0dCohwP1ndzi8DNQY2obravZ1yN8uJ+9Z9vYsDCX+Dhn/9Q7OzojpqwqyiY9KZ5Xz1y2O5QZ23e+w9FDqRiBKcjwDT316unITUQN7QPUtQ84vn4ITCIyHCQhzsUti/LYdeqyf0SNiLPr1GUS412mfigK3L60gAP1HfQNu+0OZUZ2nW4FYPPSApsjuT6TiAxH2bx0Dhe7hzh9qc/uUGZk1+lWbl6Y57hbMRvTt3nJHEY9GrHNuHedukxJbirl+Wl2h3JdJhEZjuI/e9ttnc1FksaOAc5d7mfzEuefgRrXt64sh/SkeHafjryi4qFRD3vPtrN5aUFEjA5hEpHhKPOyUlhamMGuU5H35d9l/WBFQlGIcX3+ouLdEVhUvL+ug8FRT8QciyYRGY6zeWkB++sir2x+96nWiCkKMQKzeekcmrsGqW2NrKLiSKurNInIcJzblxQw6tGIGmVh2O1hz9l2bl8SGUUhRmBut4pZI+0KfdepVjaU55KaGBnjNppEZDjOurJc0hLjrhR1RYL95zsZGImcohAjMPOzU1hSmH6lBVokaOwY4Ozl/itJNBKYRGQ4TmK8i02L8iOqbH736VYS41xmoNMotHnpHPaf76Q/QoqKd1+pq5xjcySBM4nIcKTNSwto7hrk7OXIKJvfdeoyGxZGTlGIEbjNSwoY8XjZGyHjIO46dZminBQqCiKnrtIkIsORIqlsvrlrkDOtfRFVFGIEbl1ZLqmJcRFRPOerq2yLmGbbfiYRGY5UlJPK4jnpvHzS+V9+f4ymfig6Jca7uGVRPjtPOr+oeN/5DgZGPNy+JHKK5cAkIsPB7lhRyJvnOxw/GvcLx1pYWJBGRUG63aEYIXLH8kKauwYdPxr3jmMtpCTE8Y7Fzh9fbiyTiAzHunPFXDxe5cUTl+wOZVJdAyPsPdvOnSvmRlRRiDE977qhkDiXsP1oi92hTMrrVXYcu8TmpQWOvhvrREwiMhzrxgVZzM9KZscx5375XzrRiturbF0x1+5QjBDKSUtkQ3ku2x18LB5s7ORy7zBbV0besWgSkeFYIsKdK+fyypk2x46ysP1YC/OykllV5Owbjxmzt3XlXGpb+6ht7bU7lAltP9pCQpywZVlk1Q9BiBORiGwVkVMiUisiX55g/sdF5IiIvCUie0Rk9Zh5ddb0QyJyIJRxGs61dcVcRtxedp1yXqOF/mE3r5y+bIrlYsQdy31XGjuOOa+oWFXZfqyFWxblk5mcYHc40xayRCQiccC3gbuA5cC9IrJ83GLngdtV9Ubgb4AfjJu/RVXXqOq6UMVpONu6slzy0hIdWTa/+/Rlht3eiCwKMaZvblYya0uyHXksHr/YQ2PHYMQWEYfyimg9UKuq51R1BNgG3D12AVXdo6qd1tM3gF8gucEAABHNSURBVKIQxmNEoDiXcMeKQnaebGVo1GN3OFfZfrSFvLREbirLtTsUI0y2rpjLW83dNHUO2B3KVXYcu4RL4F3LC+0OZUYkVO3iReRDwFZV/ZT1/D5gg6p+fpLlvwQsG7P8eaAb8ADfV9XxV0v+9R4EHgQoLCys2rZt24xj7uvrIz3d2U1wnR5jKOI7ctnNv1QP84XKJNbMmf3IBcGIcdSr/MFLA6yfF8/vrUyadUzjOf1zhtiM8VK/l4dfHeTeZYncWTb7IrBgxfcXrw2QliA8siFl1tsabzYxbtmypTqgEi1VDckD+BDw72Oe3wd8a5JltwAngLwx0xZYf+cAh4HbrrfPqqoqnY2dO3fOav1wcHqMoYhveNSjK7+6Xf/054eCsr1gxPjyiUta+vAz+vLJS7MPaAJO/5xVYzfGO/91t374u3uCsq1gxHe2tVdLH35Gf/jaudkHNIHZxAgc0ADyRSiL5pqB4jHPi6xpVxGRVcC/A3er6pXBnFS12frbCjyJr6jPiEGJ8S5+44Y5/Pr4JUY9XrvDAeC5ty6SnhTPJjPIaczZunIu++s7aO0ZsjsUAJ636qzuiND6IQhtHdF+YLGIlItIInAP8PTYBUSkBPglcJ+qnh4zPU1EMvz/A3cAR0MYq+Fw71s1n86BUV49Y//Yc0OjHrYfbeGOFYUkxUdWx0Fj9t63ah6q8L9HLtodCqrKU4eaqSrNYUF28IvlwiVkiUhV3cDngR34it1+pqrHROQhEXnIWuyrQB7wnXHNtAuB10TkMLAPeFZVt4cqVsP5bltSQHZqAk8evGB3KOw82UrvsJsPrl1gdyiGDRbNyWDlgkyeOnRNAU/YnbjYy+lLfXxgzXy7Q5mVkI5Zr6rPAc+Nm/a9Mf9/CvjUBOudA1aPn27ErsR4F+9bNY8nqpvoG3aTnmTf7RZ+daiZgowkNlVE1nheRvB8YM0C/vbZE5y93GfrGINPHWom3iW8d1VkJyIzsoIRMT6wZgFDo1522NiPo3tglJ0nL/P+VfOJc5lOrLHq/avnIwJPHbTvqsjjVZ46dIHblxSQm5ZoWxzBYBKRETGqSnMozk3hFzVNtsXw9JELjHi8plguxhVmJnNLRT6/qGnG67Xn1hB7zrbR0jPEB6LgWDSJyIgYIsJHqorZc7adhnZ7OhT+dH8DN8zLZOWCTFv2bzjHR24qprlrkNfPttmy/5/ubyQ7NYF3R2gn1rFMIjIiyofWFeES+NmBxrDv+2hzN0ebe7jnpmIztpzBHcsLyU5NYNv+8B+LHf0jvHDsEh9cuyDibvkwEZOIjIgyLyuFzUvn8PPqRtxh7lP0swONJMa7+MCayC8KMWYvOSGOD65dwK+PXaIjzDdvfPJgMyMeLx+9qfj6C0cAk4iMiPPRm4q51DPMS2G8jXj/sJsnDzZz18q5ZKVG3ujGRmh89KZiRjxenqgO31WRqvL4vgZWF2ezbG50FBGbRGREnN9YNof5Wcn85566sO3zlweb6R1y84mNZWHbp+F8y+Zmsr48lx/vrccTpkYLr9W2Udvax/0bS8Oyv3AwiciIOPFxLu7bWMaes+2cagn9TcpUlcdeP8+qoiwqS7JDvj8jsvzupjKaOgd5KUy3tH/s9Try0xN576p5YdlfOJhEZESke24qJinexWN7zod8X6/VtnH2cj8PbCozjRSMa7x7eSHzs5L50et1Id9XXVs/L59q5WMbSqNqeCmTiIyIlJOWyG9VLuAXNc209oZ28Mnv7z5HfnpSVJ2BGsETH+fiE5vK2HuunSNNXSHd16OvniPeJXx8Q0lI9xNuJhEZEevB2ypwe7z8x6uhuyo62NDJa7Vt/P/t3XuUVeV9xvHvb2AGMsPdsYT7zVEjBBQQWCAGYruCGkGrSTXUJESLWrEmWbVhpV20psFll+2qpdUQTY1LDZKLCUUkAVGMhJvKTRiRMJIRB1E04HCHufz6x9kTTyZz2TNzzt57zjyftWbN2Xu/7z4Pe73De/blvO/cy4fl1CdQyazZEwfTo2tnHlpblrX3eP/oaX76WgU3jBtE3x5ds/Y+cVBHJO3WsOIiPj+6P09tepuPTmbn8dmH1pbRqzCf2RNz58awZF73rvl8dcowVpW+n7X7lo++vI8ad+74zIis7D9O6oikXbtz+nmcOFvDo+v2ZXzfOysqWbP7EHMmD6MoxkFWpX342pShFBV0YtELezO+70PHTvOjzfuZOaY/g88pzPj+46aOSNq1Cz7ZnZlj+vO/v/kd71Vm7l6Ru3Pfyt30KSpgzmVDM7ZfyV29Cgu4Zepwntt5kG37j2R03//5/F6qa2u5+4qSjO43KdQRSbt3z+cuoLYW/mP1noztc+2eQ2zc93vuvqKEHl31BVYJ57bLh1PcrQv3rdxNaqbsttv7/jF+/Op+Zk8cwtDioozsM2nUEUm7N6hPIV+ZPISfba1g+zttf2rpdFUN331uN8OKi/hSjj2dJNlV1KUz3/iLEl4tP5KRGVzdne+seIOigs78XY6eDYE6IskRd11RQt/uXZn/zOucrW7bGHT/82IZ+z44wb0zR5LfSX8i0jJ/NX4Qowf25N7lpRxp4xh0z2w9wLq9H/IPMy5o93MONUV/ZZITenTN57vXjuLN947x8Eutf4R214FKFv/6La4fO5DLzz83gwmlo+jcKY9/u340laequPfZ0lbv59DR0/zrije4dGjvnH9qUx2R5Iw/v6gv117cn0Uv7GV9WcvniKk8VcWdS7bSp6iAf7r6U1lIKB3Fp/r1YN5nz2PZ9ndZ+sr+Ftevqqll3pJtnK2u5f7rR5OX47MBqyOSnLLwuk8z4txu3PX0Nt45HH7yvOqaWr7x4+28+9EpvvfXY+mdw5dBJBp3fbaEqSXFLPi/0hY9RefuLHxuN6+UH+b+61PtOddltSMysxlmtsfMysxsfgPbzcwWBdtfN7OxYeuKNKSoS2e+f/M4qmtquenRTVQcab4zqq6p5Zs/2cGLbx5iwTUjGTekTwRJJdd1yjMW3XgJfXt24SuPvcLOispm67g7/756D49vKOfWy4Yxq4PMfZW1jsjMOgEPAVcCFwE3mdlF9YpdCZQEP3OB77WgrkiDhp/bjaduncjRU1V8cfFGtrzd+KfRwyfOctuTW1i+412+NeNCbp6U29fiJVq9iwpYcuskenwiny/9YBOrS99rtOypszX847JdPLT2LW6aMJhvX9VxLg9n84xoAlDm7vvc/SywFJhVr8ws4AlP2QT0MrN+IeuKNGr0wF4s+ZtJ5OUZX/z+Rv5leSnlH574w/bjZ6p5cmM5Mx58mXV7P+Q7s0Zyx7TcGzpF4jeoTyFL505iyDmFzH1yC19fuo1dByr/8D2jM9U1LNt2gM//9zqWbN7PbZ8ZzsJrR+X8faF02Ry3ZACQPm1hBTAxRJkBIeuKNGnUgJ6svHsqC1fs5qlNb/P4hnL6FBWQV1vF4VWrqHUYM6gXP5xzKSP794w7ruSwgb0L+fkdU3hwzW/54fpylm1/l+5dO/OJvBoOr15Fda0zvLiIJ2+ZwNSSjve0pmXq279/smOzG4AZ7n5rsHwzMNHd56WVWQHc7+6/CZZfAL4FDG2ubto+5pK6rEffvn3HLV26tNWZjx8/Trduyb4xmPSMSc135HQtmw/WcPBELcfPVDGgRwGjz+3EiJ55iZxjKKnHMZ0yts6JKmfzwWoqjtfy0ckq+nUv4MI+eYws7kRejrXF6dOnb3H38c2Vy+YZ0QFgUNrywGBdmDL5IeoC4O6PAI8AjB8/3qdNm9bqwC+99BJtqR+FpGdMcr7rgt9JzlhHGTMjqRmvDn4nNV+6KDJm8x7Rq0CJmQ0zswLgRmB5vTLLgS8HT89NAird/WDIuiIikgOydkbk7tVmNg9YBXQCHnP3UjO7Pdi+GFgJXAWUASeBOU3VzVZWERGJT1YnWXH3laQ6m/R1i9NeO3Bn2LoiIpJ7NLKCiIjESh2RiIjESh2RiIjESh2RiIjESh2RiIjESh2RiIjESh2RiIjEKmtjzcXBzD4A3m7DLoqBlk/tGa2kZ0x6PlDGTFHGtkt6PmhbxiHu3uworjnVEbWVmb0WZoC+OCU9Y9LzgTJmijK2XdLzQTQZdWlORERipY5IRERipY7ojz0Sd4AQkp4x6flAGTNFGdsu6fkggoy6RyQiIrHSGZGIiMSqQ3REZjbDzPaYWZmZzW9gu5nZomD762Y2NmzdCDPODrLtNLMNZjYmbVt5sH67mb0WY8ZpZlYZ5NhuZgvC1o0w4z1p+XaZWY2Z9Qm2Zf04mtljZnbIzHY1sj0JbbG5jLG2xRD5ktAOm8sYazsM3meQma01szfMrNTM7m6gTDTt0d1z+ofUxHpvAcOBAmAHcFG9MlcBvwQMmARsDls3woyTgd7B6yvrMgbL5UBxAo7jNGBFa+pGlbFe+WuAFyM+jpcDY4FdjWyPtS2GzBh3W2wuX6ztMEzGuNth8D79gLHB6+7Ab+P6v7EjnBFNAMrcfZ+7nwWWArPqlZkFPOEpm4BeZtYvZN1IMrr7Bnc/EixuAgZmIUebMmapbjYz3gQ8nYUcjXL3l4HDTRSJuy02mzHuthjiGDYmMcewnsjbIYC7H3T3rcHrY8BuYEC9YpG0x47QEQ0A3klbruBPD3ZjZcLUjSpjultIfUqp48AaM9tiZnOzkA/CZ5wcnML/0sxGtrBuVBkxs0JgBvBM2uoojmNz4m6LLRVHWwwjznYYWlLaoZkNBS4BNtfbFEl7zOpU4ZJ5Zjad1B//ZWmrL3P3A2b2Z8DzZvZm8IksaluBwe5+3MyuApYBJTHkCOMaYL27p39qTcpxbBcS3BbVDlvAzLqR6gi/7u5Hs/U+TekIZ0QHgEFpywODdWHKhKkbVUbMbDTwA2CWu/++br27Hwh+HwJ+Qeq0OfKM7n7U3Y8Hr1cC+WZWHKZuVBnT3Ei9yyERHcfmxN0WQ4m5LTYpAe2wJWJth2aWT6oT+pG7/7yBItG0x2zfEIv7h9RZ3z5gGB/fVBtZr8zV/PENuVfC1o0w42CgDJhcb30R0D3t9QZgRkwZP8nH302bAOwPjmlijmNQriep6/dFUR/HYP9DafxGe6xtMWTGWNtiiHyxtsMwGRPSDg14AniwiTKRtMecvzTn7tVmNg9YRepJj8fcvdTMbg+2LwZWkno6pAw4Ccxpqm5MGRcA5wAPmxlAtacGIuwL/CJY1xlY4u6/iinjDcAdZlYNnAJu9FSrTdJxBLgOWO3uJ9KqR3IczexpUk91FZtZBfDPQH5avljbYsiMsbbFEPlibYchM0KM7TAwBbgZ2Glm24N13yb1QSPS9qiRFUREJFYd4R6RiIgkmDoiERGJlToiERGJlToiERGJlToiERGJlToiERGJlToikQwws15m9rdpy/3N7GdZeq9r06c2aGD7p83s8Wy8t0g26HtEIhkQDBq5wt1HRfBeG4CZ7v5hE2XWAF9z9/3ZziPSVjojEsmM+4ERwWRmD5jZ0LpJ0czsq2a2zMyeDyY9m2dm3zSzbWa2KW1CtBFm9qtg1OV1ZnZh/Tcxs/OBM3WdkJl9IZhYbYeZpQ+M+SypccxEEk8dkUhmzAfecveL3f2eBraPAv4SuBRYCJx090uAjcCXgzKPAHe5+zjg74GHG9jPFFKjS9dZAHzO3ccAM9PWvwZMbcO/RyQyOT/WnEhCrPXU5GPHzKyS1BkLwE5gdDAU/2Tgp8E4YwBdGthPP+CDtOX1wONm9hMgffTkQ0D/DOYXyRp1RCLROJP2ujZtuZbU32Ee8JG7X9zMfk6RGrUZAHe/3cwmkholeYuZjfPUtAxdg7IiiadLcyKZcQzo3trKnpqQ7Hdm9gUASxnTQNHdwHl1C2Y2wt03u/sCUmdKdXPEnA/sam0ekSipIxLJgOAsZH3w4MADrdzNbOAWM9sBlAKzGijzMnCJfXz97gEz2xk8GLGB1LwwANOB51qZQyRSenxbpJ0xs/8CnnX3NY1s7wL8mtSU09WRhhNpBZ0RibQ/9wGFTWwfDMxXJyTthc6IREQkVjojEhGRWKkjEhGRWKkjEhGRWKkjEhGRWKkjEhGRWP0/jMv9DeSrvDsAAAAASUVORK5CYII=",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad29b88ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\\l examples/matplotlibexample.p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we have an example of a simple wrapper for matplotlib.pyplot written in q (in `matplotlib.q`) which lets us use pyplot methods and functions like q functions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fad29b84a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "/ This is q code\n",
    "\\l examples/importmatplotlib.q\n",
    "pi:2*asin 1\n",
    "x:til[200]%100\n",
    "y:1+sin 2*pi*x\n",
    "plt:.matplotlib.pyplot[]\n",
    "plt.\n",
    "/\\t plt:.p.module .p.import`matplotlib.pyplot\n",
    "plt.plot[x;y];\n",
    "plt.xlabel\"X values\";\n",
    "plt.ylabel\"Y values\";\n",
    "plt.title\"A pyplot chart created in q\";\n",
    "plt.grid 1b;\n",
    "plt.show[];"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Q 3.5",
   "language": "q",
   "name": "qpk"
  },
  "language_info": {
   "file_extension": ".q",
   "mimetype": "text/x-q",
   "name": "q",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
